Disease Detection in Apple Leaves Using Image Processing Techniques
Received: 28 December 2021 | Revised: 26 January 2022 | Accepted: 1 February 2022 | Online: 9 April 2022
Corresponding author: M. Alghamdi
The agricultural sector in Saudi Arabia constitutes an essential pillar of the national economy and food security. Crop diseases are a major problem of the agricultural sector and greatly affect the development of the economies in various countries around the world. This study employed three prediction models, namely CNN, SVM, and KNN, with different image processing methods to detect and classify apple plant leaves as healthy or diseased. These models were evaluated using the Kaggle New Plant Diseases database. This study aims to help farmers detect and prevent diseases from spreading. The proposed method provides recommendations for the appropriate solutions for each type of recognized plant disease based on the classification results.
Keywords:Machine learning, Disease of Plant, Apple, Deep learning
B. F. Johnston and J. W. Mellor, "The Role of Agriculture in Economic Development," The American Economic Review, vol. 51, no. 4, pp. 566–593, 1961.
S. Zafar, "Agricultural Biomass in MENA - EcoMENA," Jul. 04, 2020. https://www.ecomena.org/agricultural-resources-mena/ (accessed Feb. 17, 2022).
S. K. Sao and S. Patil, "A Survey on Classification Techniques for Plant Disease Detection using Image Processing," International Journal for Scientific Research and Development, vol. 3, pp. 1122–1125, 2015.
J. Bruinsma, "The resource outlook to 2050: by how much do land, water and crop yields need to increase by 2050?," in How to feed the World in 2050. Proceedings of a technical meeting of experts, Rome, Italy, Jun. 2009, pp. 1–33.
A. H Kulkarni and R. K. A. Patil, "Applying image processing technique to detect plant diseases," International Journal of Modern Engineering Research, vol. 2, no. 5, pp. 3661–3664, Sep. 2012.
X. E. Pantazi, D. Moshou, and A. A. Tamouridou, "Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers," Computers and Electronics in Agriculture, vol. 156, pp. 96–104, Jan. 2019. DOI: https://doi.org/10.1016/j.compag.2018.11.005
G. Saradhambal, R. Dhivya, S. Latha, and R. Rajesh, "Plant disease detection and its solution using image classification," International Journal of Pure and Applied Mathematics, vol. 119, pp. 879–883, Jan. 2018.
N. Ganatra and A. Patel, "A Multiclass Plant Leaf Disease Detection using Image Processing and Machine Learning Techniques," International Journal on Emerging Technologies, vol. 11, no. 2, pp. 1082–1086, 2020.
S. D. Khirade and A. B. Patil, "Plant Disease Detection Using Image Processing," in 2015 International Conference on Computing Communication Control and Automation, Pune, India, Oct. 2015, pp. 768–771. DOI: https://doi.org/10.1109/ICCUBEA.2015.153
V. K. Vishnoi, K. Kumar, and B. Kumar, "Plant disease detection using computational intelligence and image processing," Journal of Plant Diseases and Protection, vol. 128, no. 1, pp. 19–53, Oct. 2021. DOI: https://doi.org/10.1007/s41348-020-00368-0
S. Ramesh et al., "Plant Disease Detection Using Machine Learning," in 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Apr. 2018, pp. 41–45. DOI: https://doi.org/10.1109/ICDI3C.2018.00017
N. C. Eli-Chukwu, "Applications of Artificial Intelligence in Agriculture: A Review," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4377–4383, Aug. 2019. DOI: https://doi.org/10.48084/etasr.2756
T. Fang, P. Chen, J. Zhang, and B. Wang, "Identification of Apple Leaf Diseases Based on Convolutional Neural Network," in Intelligent Computing Theories and Application, 2019, pp. 553–564. DOI: https://doi.org/10.1007/978-3-030-26763-6_53
P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, "Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks," IEEE Access, vol. 7, pp. 59069–59080, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2914929
B. Dai, T. Qiu, and K. Ye, "Foliar Disease Classification," 2020.
B. Liu, Y. Zhang, D. He, and Y. Li, "Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks," Symmetry, vol. 10, no. 1, Jan. 2018. DOI: https://doi.org/10.3390/sym10010011
F. Mohameth, C. Bingcai, and K. A. Sada, "Plant Disease Detection with Deep Learning and Feature Extraction Using Plant Village," Journal of Computer and Communications, vol. 8, no. 6, pp. 10–22, Jun. 2020. DOI: https://doi.org/10.4236/jcc.2020.86002
L. Loyani and D. Machuve, "A Deep Learning-based Mobile Application for Segmenting Tuta Absoluta’s Damage on Tomato Plants," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7730–7737, Oct. 2021. DOI: https://doi.org/10.48084/etasr.4355
M. H. Saleem, J. Potgieter, and K. M. Arif, "Plant Disease Detection and Classification by Deep Learning," Plants, vol. 8, no. 11, Nov. 2019, Art. no. 468. DOI: https://doi.org/10.3390/plants8110468
S. Nuanmeesri, "A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7678–7683, Oct. 2021. DOI: https://doi.org/10.48084/etasr.4455
P. Kulkarni, A. Karwande, T. Kolhe, S. Kamble, A. Joshi, and M. Wyawahare, "Plant Disease Detection Using Image Processing and Machine Learning," arXiv:2106.10698 [cs], Nov. 2021.
V. Singh and A. K. Misra, "Detection of plant leaf diseases using image segmentation and soft computing techniques," Information Processing in Agriculture, vol. 4, no. 1, pp. 41–49, Nov. 2017. DOI: https://doi.org/10.1016/j.inpa.2016.10.005
R. Sujatha, Y. S. Kumar, and G. U. Akhil, "Leaf disease detection using image processing," Journal of Chemical and Pharmaceutical Sciences, vol. 10, no. 1, pp. 670–672, Jan. 2017.
C. Connolly and T. Fleiss, "A study of efficiency and accuracy in the transformation from RGB to CIELAB color space," IEEE Transactions on Image Processing, vol. 6, no. 7, pp. 1046–1048, Jul. 1997. DOI: https://doi.org/10.1109/83.597279
P. Chaudhary, A. K. Chaudhari, D. A. N. Cheeran, and S. Godara, "Color Transform Based Approach for Disease Spot Detection on Plant Leaf," International Journal of Computer Science and Telecommunications, vol. 3, no. 6, pp. 65–70.
N. N. Kurniawati, S. N. H. S. Abdullah, S. Abdullah, and S. Abdullah, "Investigation on Image Processing Techniques for Diagnosing Paddy Diseases," in 2009 International Conference of Soft Computing and Pattern Recognition, Sep. 2009, pp. 272–277. DOI: https://doi.org/10.1109/SoCPaR.2009.62
L. Liu and G. Zhou, "Extraction of the Rice Leaf Disease Image Based on BP Neural Network," in 2009 International Conference on Computational Intelligence and Software Engineering, Wuhan, China, Sep. 2009, pp. 1–3. DOI: https://doi.org/10.1109/CISE.2009.5363225
Z. Ma, J. M. R. S. Tavares, and R. M. N. Jorge, "A Review on the Current Segmentation Algorithms for Medical Images," presented at the International Conference on Imaging Theory and Applications, Feb. 2009, vol. 1, pp. 135–140.
D. Kaur and Y. Kaur, "Various Image Segmentation Techniques: A Review," International Journal of Computer Science and Mobile Computing, vol. 3, no. 5, pp. 809–814, May 2014.
S. Saini and K. Arora, "A Study Analysis on the Different Image Segmentation Techniques," International Journal of Information & Computation Technology., vol. 4, no. 1, pp. 1445–1452, 2014.
J. A. M. Saif, A. A. M. Al-Kubati, A. S. Hazaa, and N. Al-Moraish, "Image Segmentation Using Edge Detection and Thresholding - ACIT," in The 13th International Arab Conference on Information Technology, Abhu Dhabi, UAE, Dec. 2012, pp. 473–476.
N. Dhanachandra, K. Manglem, and Y. J. Chanu, "Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm," Procedia Computer Science, vol. 54, pp. 764–771, Jan. 2015. DOI: https://doi.org/10.1016/j.procs.2015.06.090
P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, "Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey," Procedia Computer Science, vol. 167, pp. 516–530, Jan. 2020. DOI: https://doi.org/10.1016/j.procs.2020.03.308
U. Pratap Singh, Kanak Saxena, and Sanjeev Jain, "Semi-supervised Method of Multiple Object Segmentation with a Region Labeling and Flood Fill," Signal & Image Processing : An International Journal, vol. 2, no. 3, pp. 175–193, Sep. 2011. DOI: https://doi.org/10.5121/sipij.2011.2314
J. Singh and H. Kaur, "A review on: Various techniques of plant leaf disease detection," in 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, Jan. 2018, pp. 232–238. DOI: https://doi.org/10.1109/ICISC.2018.8399069
M. Islam, A. Dinh, K. Wahid, and P. Bhowmik, "Detection of potato diseases using image segmentation and multiclass support vector machine," in 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada, Apr. 2017, pp. 1–4. DOI: https://doi.org/10.1109/CCECE.2017.7946594
H. Al Hiary, S. Bani Ahmad, M. Reyalat, M. Braik, and Z. ALRahamneh, "Fast and Accurate Detection and Classification of Plant Diseases," International Journal of Computer Applications, vol. 17, no. 1, pp. 31–38, Mar. 2011. DOI: https://doi.org/10.5120/2183-2754
J. D. Pujari, R. Yakkundimath, and A. S. Byadgi, "SVM and ANN based classification of plant diseases using feature reduction technique," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 3, no. 7, pp. 6–15, Jun. 2016. DOI: https://doi.org/10.9781/ijimai.2016.371
D. S. Guru, Y. H. Sharath, and S. Manjunath, "Texture Features and KNN in Classification of Flower Images," IJCA Special Issue on "Recent Trends in Image Processing and Pattern Recognition," pp. 21–29, 2010.
K. R. Gavhale and U. Gawande, "An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques," IOSR Journal of Computer Engineering, vol. 16, no. 1, pp. 10–16, 2014. DOI: https://doi.org/10.9790/0661-16151016
C. H.Arun, W. R. Sam Emmanuel, and D. Christopher Durairaj, "Texture Feature Extraction for Identification of Medicinal Plants and comparison of different classifiers," International Journal of Computer Applications, vol. 62, no. 12, pp. 1–9, Jan. 2013. DOI: https://doi.org/10.5120/10129-4920
S. Singh and S. Gupta, "Apple Scab and Marsonina Coronaria Diseases Detection in Apple Leaves Using Machine Learning," International Journal of Pure and Applied Mathematics, pp. 1151–1166, Jan. 2018.
C. Szegedy et al., "Going Deeper With Convolutions," presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, US, 2015, pp. 1–9. DOI: https://doi.org/10.1109/CVPR.2015.7298594
How to Cite
MetricsAbstract Views: 2043
PDF Downloads: 1197
Copyright (c) 2022 S. Alqethami, B. Almtanni, W. Alzhrani, M. Alghamdi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.